{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If running within a Docker, no action is needed. If not, add the installation path (e.g. 'path_to_fwi3d_directory/local/bin') to the environment variables 'PATH' and 'PYTHONPATH' before running the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "\n",
    "import seppy\n",
    "\n",
    "sep = seppy.sep()\n",
    "\n",
    "# Create a directory to store data\n",
    "!mkdir -p data\n",
    "datapath = \"./data/\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate source time function (Ricker wavelet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# source time function parameters\n",
    "nt=1001 # number of time samples\n",
    "dt=0.004 # sampling rate (sec)\n",
    "ot=0.0 # origin time\n",
    "f0=10. # peak frequency (Hz)\n",
    "shift=50 # shift wavelet (number of samples)\n",
    "\n",
    "# source wavelet\n",
    "w=np.zeros(nt)\n",
    "pi = math.pi\n",
    "tshift=shift*dt\n",
    "sig=math.sqrt(2)/(2*f0*pi)\n",
    "t=np.linspace(0,(nt-1)*dt,nt)\n",
    "w=(1.0 - ((t-tshift)/sig)**2)*np.exp( -0.5*((t-tshift)/sig)**2)\n",
    "\n",
    "plt.plot(np.linspace(ot,(nt-1)*dt,nt),w)\n",
    "plt.xlabel(\"Time (sec)\")\n",
    "plt.ylabel(\"Amplitude\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate elastic isotropic models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1800 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nz=101\n",
    "nx=101\n",
    "ny=101\n",
    "dz=0.02\n",
    "dx=0.02\n",
    "dy=0.02\n",
    "oz=0.0\n",
    "ox=0.0\n",
    "oy=0.0\n",
    "extent = [ox,ox+(nx-1)*dx,oz+(nz-1)*dz,oz]\n",
    "\n",
    "# two-layers model\n",
    "model0=np.zeros((3,ny,nx,nz))\n",
    "model0[0,:,:,:50] = 2.5 # Vp in km/s\n",
    "model0[0,:,:,50:] = 4.0\n",
    "model0[1,:,:,:50] = 1.5 # Vs in km/s\n",
    "model0[1,:,:,50:] = 2.0\n",
    "model0[2,:,:,:50] = 1.0 # Density in g/cc\n",
    "model0[2,:,:,50:] = 2.0 # Density in g/cc\n",
    "\n",
    "model=model0[:,50,:,:]\n",
    "\n",
    "plt.figure(figsize=(8,6),dpi=300)\n",
    "plt.subplot(3,1,1)\n",
    "plt.imshow(np.transpose(model[0,:,:]),interpolation='none',aspect=\"auto\",extent=extent,cmap='jet',vmin=2.5,vmax=4.0)\n",
    "plt.xlabel('X (km)')\n",
    "plt.ylabel('Depth (km)')\n",
    "plt.colorbar(fraction=0.04, format='%.'+str(1)+'f',label=r'$V_p$ (km/s)')\n",
    "plt.title(\"Vp\")\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "plt.imshow(np.transpose(model[1,:,:]),interpolation='none',aspect=\"auto\",extent=extent,cmap='jet',vmin=1.5,vmax=2.0)\n",
    "plt.xlabel('X (km)')\n",
    "plt.ylabel('Depth (km)')\n",
    "plt.colorbar(fraction=0.04, format='%.'+str(1)+'f',label=r'$V_s$ (km/s)')\n",
    "plt.title(\"Vs\")\n",
    "\n",
    "plt.subplot(3,1,3)\n",
    "plt.imshow(np.transpose(model[2,:,:]),interpolation='none',aspect=\"auto\",extent=extent,cmap='jet',vmin=1.0,vmax=2.0)\n",
    "plt.xlabel('X (km)')\n",
    "plt.ylabel('Depth (km)')\n",
    "plt.colorbar(fraction=0.04, format='%.'+str(1)+'f',label=r'$\\rho$ (g/cc)')\n",
    "plt.title(\"Density\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save wavelet and models to SEPlib\n",
    "sep.write_file(datapath+\"wavelet.H\", w, ds=np.array([dt]), os=np.array([ot]), dpath=datapath)\n",
    "sep.write_file(datapath+\"model0.H\", np.transpose(model0), ds=np.array([dz,dx,dy,1]), os=np.array([oz,ox,oy,0]), dpath=datapath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model seismic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "[[60190,1],0]: A high-performance Open MPI point-to-point messaging module\n",
      "was unable to find any relevant network interfaces:\n",
      "\n",
      "Module: OpenFabrics (openib)\n",
      "  Host: thanos\n",
      "\n",
      "Another transport will be used instead, although this may result in\n",
      "lower performance.\n",
      "\n",
      "NOTE: You can disable this warning by setting the MCA parameter\n",
      "btl_base_warn_component_unused to 0.\n",
      "--------------------------------------------------------------------------\n",
      "\n",
      "====================\n",
      "Size of MPI communicator = 1 ; current rank = 0\n",
      "====================\n",
      "\n",
      "====================\n",
      "Tue Oct 24 00:49:15 2023\n",
      "\n",
      "====================\n",
      "\n",
      "==========================\n",
      " Subsurface model geometry\n",
      "==========================\n",
      "xmin=0.00000 km, xmax=2.00000 km, dx=0.02000 km, nx=101\n",
      "ymin=0.00000 km, ymax=2.00000 km, dy=0.02000 km, ny=101\n",
      "zmin=0.00000 km, zmax=2.00000 km, dz=0.02000 km, nz=101\n",
      "\n",
      "==========================\n",
      " Boundary conditions\n",
      "==========================\n",
      "Top boundary condition = free surface\t taper size = 0\n",
      "Bottom boundary condition = locally absorbing\t taper size = 25\n",
      "Left boundary condition = locally absorbing\t taper size = 25\n",
      "Right boundary condition = locally absorbing\t taper size = 25\n",
      "Front boundary condition = locally absorbing\t taper size = 25\n",
      "Back boundary condition = locally absorbing\t taper size = 25\n",
      "\n",
      "==========================\n",
      " Sources' and receivers' geometry\n",
      "==========================\n",
      "Sources' and receivers' coordinates are read from file ./par/srcoord.txt\n",
      "Receivers are point measurement of type particle velocity\n",
      "Number of sources = 2\n",
      "Shot 0 located at x=0.50000 km, y=0.50000 km, z=0.10000 km, has 5 receivers\n",
      "Shot 1 located at x=0.60000 km, y=0.50000 km, z=0.10000 km, has 5 receivers\n",
      "Total number of 3-components receivers to be modeled = 10\n",
      "\n",
      "==========================\n",
      " Source wavelet\n",
      "==========================\n",
      "Vector force is assumed with a dip of 1.570800 rad clockwise wrt to the horizontal and azimuth of 0.000000 rad anti-clockwise wrt to the x-axis\n",
      "Input wavelet will be duplicated 3 x 2 times with the appropriate scaling\n",
      "Parameters mxx, myy, mzz, mxy, mxz, myz are ignored\n",
      "\n",
      "Start modeling shot 0 by process 0\n",
      "\n",
      "==========================\n",
      " Subsurface model bounds\n",
      "==========================\n",
      "Model is assumed to contain Vp (km/s), Vs (km/s) and density Rho (g/cc) in that order\n",
      "Vp bounds are 2.50 - 4.00 km/s\n",
      "Vs bounds are 1.50 - 2.00 km/s\n",
      "Rho bounds are 1.00 - 2.00 g/cc\n",
      "Vp bounds after hard clipping are 2.50 - 4.00 km/s\n",
      "Vs bounds after hard clipping are 1.50 - 2.00 km/s\n",
      "Rho bounds after hard clipping are 1.00 - 2.00 g/cc\n",
      "\n",
      "==========================\n",
      " Dispersion analysis\n",
      "==========================\n",
      "Maximum frequency assumed by the user = 10.00 Hz\n",
      "Corresponding minimum number of grid points per wavelength according to minimum velocity = 7.5\n",
      "\n",
      "==========================\n",
      " Time analysis\n",
      "==========================\n",
      "Maximum duration of the input = 4.000 sec, sampling = 0.00400 sec, number of samples = 1001\n",
      "Original Courant number = 0.50\n",
      "Corresponding propagation time step based on CFL condition = 0.00250 sec\n",
      "Updated Courant number = 0.40\n",
      "Maximum duration of the propagation = 4.000 sec, sampling = 0.00200 sec, number of samples = 2001\n",
      "Time resampling method = sinc interpolation\n",
      "Propagation progress = 10%\n",
      "Propagation progress = 20%\n",
      "Propagation progress = 30%\n",
      "Propagation progress = 40%\n",
      "Propagation progress = 50%\n",
      "Propagation progress = 60%\n",
      "Propagation progress = 70%\n",
      "Propagation progress = 80%\n",
      "Propagation progress = 90%\n",
      "Propagation progress = 100%\n",
      "\n",
      "Start modeling shot 1 by process 0\n",
      "\n",
      "==========================\n",
      " Subsurface model bounds\n",
      "==========================\n",
      "Model is assumed to contain Vp (km/s), Vs (km/s) and density Rho (g/cc) in that order\n",
      "Vp bounds are 2.50 - 4.00 km/s\n",
      "Vs bounds are 1.50 - 2.00 km/s\n",
      "Rho bounds are 1.00 - 2.00 g/cc\n",
      "Vp bounds after hard clipping are 2.50 - 4.00 km/s\n",
      "Vs bounds after hard clipping are 1.50 - 2.00 km/s\n",
      "Rho bounds after hard clipping are 1.00 - 2.00 g/cc\n",
      "\n",
      "==========================\n",
      " Dispersion analysis\n",
      "==========================\n",
      "Maximum frequency assumed by the user = 10.00 Hz\n",
      "Corresponding minimum number of grid points per wavelength according to minimum velocity = 7.5\n",
      "\n",
      "==========================\n",
      " Time analysis\n",
      "==========================\n",
      "Maximum duration of the input = 4.000 sec, sampling = 0.00400 sec, number of samples = 1001\n",
      "Original Courant number = 0.50\n",
      "Corresponding propagation time step based on CFL condition = 0.00250 sec\n",
      "Updated Courant number = 0.40\n",
      "Maximum duration of the propagation = 4.000 sec, sampling = 0.00200 sec, number of samples = 2001\n",
      "Time resampling method = sinc interpolation\n",
      "Propagation progress = 10%\n",
      "Propagation progress = 20%\n",
      "Propagation progress = 30%\n",
      "Propagation progress = 40%\n",
      "Propagation progress = 50%\n",
      "Propagation progress = 60%\n",
      "Propagation progress = 70%\n",
      "Propagation progress = 80%\n",
      "Propagation progress = 90%\n",
      "Propagation progress = 100%\n",
      "\n",
      "====================\n",
      "Tue Oct 24 00:50:46 2023\n",
      "\n",
      "====================\n"
     ]
    }
   ],
   "source": [
    "!WE_MODELING_3D.x source=./data/wavelet.H model=./data/model0.H output=./data/data.H parfile=./par/elastic_modeling.p datapath=${PWD}/data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load, and plot\n",
    "ns=2 # number of shots\n",
    "ncomp=3 # number of components\n",
    "axes_data, data = sep.read_file(datapath+\"data.H\")\n",
    "data = data.reshape([axes_data.n[0],int(axes_data.n[1]/(ns*ncomp)),ncomp,ns],order='F').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1200 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=0\n",
    "d=data[s,:,:,:]\n",
    "vmax=np.amax(d)\n",
    "p=0.1\n",
    "\n",
    "plt.figure(figsize=(8,4),dpi=300)\n",
    "plt.subplot(1,3,1)\n",
    "plt.imshow(np.transpose(d[0]),interpolation='none',aspect=\"auto\",cmap='Greys',vmax=p*vmax,vmin=-p*vmax)\n",
    "plt.xlabel('Trace number')\n",
    "plt.ylabel('Time sample')\n",
    "plt.title(\"Shot #%s, x-component\" %s)\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.imshow(np.transpose(d[1]),interpolation='none',aspect=\"auto\",cmap='Greys',vmax=p*vmax,vmin=-p*vmax)\n",
    "plt.xlabel('Trace number')\n",
    "plt.ylabel('Time sample')\n",
    "plt.title(\"Shot #%s, y-component\" %s)\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.imshow(np.transpose(d[2]),interpolation='none',aspect=\"auto\",cmap='Greys',vmax=p*vmax,vmin=-p*vmax)\n",
    "plt.xlabel('Trace number')\n",
    "plt.ylabel('Time sample')\n",
    "plt.title(\"Shot #%s, z-component\" %s)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean up the data directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm -rf data"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
